METHODS AND APPARATUS TO TRANSLATE MODELS FOR EXECUTION ON A SIMULATION PLATFORM
Methods and apparatus are disclosed to translate a model. An example method includes applying an independent variable and a dependent variable to the model and estimating a value for a model parameter, generating predicted best fit data points based on the model parameter, values associated with the independent variable, and values associated with the dependent variable, estimating coefficients based on the best fit data points, and generating a spline comprising a plurality of polynomial functions based on the coefficients.
1 . A method to translate a model to a spline, comprising:
applying an independent variable and a dependent variable to the model and estimating a value for a model parameter;
generating predicted best fit data points based on the model parameter, values associated with the independent variable, and values associated with the dependent variable;
estimating coefficients based on the best fit data points; and
generating a spline comprising a plurality of polynomial functions based on the coefficients.
2 . A method as described in claim 1 , wherein the model comprises a marketing mix model.
3 . A method as described in claim 2 , wherein the marketing mix model comprises a volumetric decomposition based on a marketing driver.
4 . A method as described in claim 1 , wherein the independent variable is associated with a marketing driver.
5 . A method as described in claim 4 , further comprising utilizing gross rating points as a unit of measure for the market driver.
6 . A method as described in claim 4 , wherein the marketing driver comprises at least one of television advertisements, newspaper advertisements, coupons, or in-store promotions.
7 . A method as described in claim 1 , wherein generating the spline further comprises generating the plurality of polynomial equations to respectively fit a plurality of knot range subsets.
8 . A method as described in claim 1 , further comprising extrapolating the predicted best fit data points beyond an original range associated with the independent variable and the dependent variable.
9 . A method as described in claim 1 , further comprising formatting the coefficients to match input requirements of a simulator.
10 . A method as described in claim 9 , further comprising associating subsets of the coefficients with corresponding ones of a plurality of knot range subsets.
11 . An apparatus to translate a model to a spline, comprising:
a source model lift data engine to apply an independent variable to the model, and a source model causal data engine to apply a dependent variable to the model;
a parameter estimator to estimate a value for a model parameter, and to generate predicted best fit data points based on the model parameter, based on values associated with the independent variable, and based on values associated with the dependent variable; and
a spline engine to estimate coefficients based on the best fit data points, and to generate a spline comprising a plurality of polynomial functions based on the coefficients.
12 . An apparatus as described in claim 11 , further comprising a source model equation engine to identify the model parameter associated with the model.
13 . An apparatus as described in claim 12 , wherein the source model equation engine receives a functional form of the model.
14 . An apparatus as described in claim 11 , wherein the spline engine generates the plurality of polynomial functions to respectively fit a plurality of knot range subsets.
15 . An apparatus as described in claim 11 , further comprising an extrapolator to extrapolate the predicted best fit data points beyond an original range associated with the independent variable and the dependent variable.
16 . An apparatus as described in claim 11 , further comprising a spline output formatter to format the coefficients to match input requirements of a simulator.
17 . A tangible machine accessible medium having instructions stored thereon that, when executed, cause a machine to, at least:
apply an independent variable and a dependent variable to the model and estimating a value for a model parameter;
generate predicted best fit data points based on the model parameter, values associated with the independent variable, and values associated with the dependent variable;
estimate coefficients based on the best fit data points; and
generate a spline comprising a plurality of polynomial functions based on the coefficients.
18 . A tangible machine accessible medium as described in claim 17 having instructions stored thereon that, when executed, cause a machine to generate a spline for a marketing mix model.
19 . A tangible machine accessible medium as described in claim 18 having instructions stored thereon that, when executed, cause a machine to process the marketing mix model with a volumetric decomposition based on a marketing driver.
20 . A tangible machine accessible medium as described in claim 17 having instructions stored thereon that, when executed, cause a machine to utilize gross rating points as a unit of measure for a market driver.
21 . A tangible machine accessible medium as described in claim 17 having instructions stored thereon that, when executed, cause a machine to generate the plurality of polynomial equations to respectively fit a plurality of knot range subsets.
22 . A tangible machine accessible medium as described in claim 17 having instructions stored thereon that, when executed, cause a machine to extrapolate the predicted best fit data points beyond an original range associated with the independent variable and the dependent variable.
23 . A tangible machine accessible medium as described in claim 17 having instructions stored thereon that, when executed, cause a machine to format the coefficients to match input requirements of a simulator.
24 . A tangible machine accessible medium as described in claim 23 having instructions stored thereon that, when executed, cause a machine to associate subsets of the coefficients with corresponding ones of a plurality of knot range subsets.